1. Field of the Invention
The present invention relates in general to the field of energy recovery systems, and in particular, systems, program product, and methods related to determining, calculating, and modeling energy consumption requirements of a non-thermodynamically constrained process or cluster of processes.
2. Description of the Related Art
Many different types of processes consume energy to obtain an output result, or to produce a required product or compound. For example, chemical processes consume energy to provide a desired result. For large-scale processes that consume significant amounts of energy, it is preferable to minimize the energy consumed, where possible. In the case of, for example, electrical energy generation systems or relatively large manufacturing plant or factories, it is preferable to optimize and potentially minimize the consumption of energy through careful operation, design or reconfiguration of the plant and equipment used, taking into considerations process constraints such as safety, health and environment.
For example, in some industrial manufacturing processes, specific streams of material flows need to be supplied to different types of equipment and machinery at specific temperatures. These material flows may need to be heated or cooled from an original starting temperature to a target temperature. This, in turn, will require the consumption of energy to not only cool specific streams, but also to heat other specific streams while monitoring for corrosion problems, scaling problems, streams location, environmental compliances, and hazardous situations.
The total energy employed or consumed by the industrial manufacturing processes can be optimized to a global minimal level through, for example, careful placement and configuration of specific material streams with respect to one another. For example, there may be the potential for hot streams that require cooling to be placed in proximity with cold streams that require heating. Streams having thermal energy already present that needs to be removed, or streams that need to have heat added, can be associated with one another to optimize the energy consumption of the process. In addition, the minimum temperature differences between hot streams and cold streams upon optimization can also result in huge savings in energy consumption. These savings, however, are not realizable as long as there are some constraints that may prevent some of the streams from being matched with some others, and therefore, needs some consideration. These “non-thermodynamic” constraints, for example, can be related to corrosion, environmental reasons, scaling, leakage problems, streams far apart, or streams in different hazardous zones, etc.
These considerations can be taken into account during the energy targeting phase prior to design, or alternatively, during the reconfiguration or refitting of the plant or equipment. It is by the inventor that it would be preferable to consider these optimization issues with a modeling system prior to the actual design, redesign, construction or modification of the actual plant and equipment. State-of-the-art software currently on the market, including AspenTech Inc. software Aspen Pinch and HX-NET Pinch Express of KBC and Sprint of UMIST, however, do not address such targeting problem for non-thermodynamically constrained process situations under all possible combinations of solutions in the process, which can include the modifying resource stream conditions of one or more of all streams and/or adding one or more heat carriers at different flow and supply temperatures. In the targeting phase, these software products only allow specific stream conditions of a process to be tracked and individual operational attributes associated with these streams to be modeled and adjusted, if required. In general terms, such software products are normally employed to track the temperatures and heat capacity flows of specific material streams in a process. Although such software can provides useful tools, they are not particularly flexible in application, and do not address some of the above problems systematically.
For example, in grassroots heat exchanger network design, the parameter known as global ΔTmin (referred to interchangeably as minimum approach temperature or minimum temperature approach) is typically used in the state-of-the-art commercial software to represent the desired level of heat recovery between hot and cold streams, such as, for example, the minimum temperature difference allowed to recover energy through a heat exchanger. A single value for a specific material stream attribute can only be adjusted at one time including the ΔTmin. This forces a user of the system to employ a trial and error approach through ‘tweaking’ particular attributes of specific streams one at a time, to hopefully arrive at an optimized value for Qc and/or Qh, which represent the total energy consumed for heating (Qh) and the total energy consumed for cooling (Qc) for the process model. This limitation becomes compounded and makes the existing software difficult to employ effectively in large-scale processes, which employ many material streams, where these material streams may have a number of operational attributes that can be modeled and adjusted. This is also the case when several sets of stream-specific minimum temperature approaches need to be analyzed for optimal energy recovery for constrained waste energy recovery system.
Further, process stream changes not only may result in reduction in energy utility, but may also bring reduction in ΔTmin. Thus, in current state-of-the art technology, the capital/energy trade-off in energy system synthesis must be readjusted after each process change. Recognized by the inventor is that the change in the driving force distribution due to both stream-specific ΔTmin and each process change also affects the utility level selection, and that the problem is interlinked and multi-dimensional in that process changes and stream-specific ΔTmin selection are often competing for optimal selection of utilities, optimal process conditions, and optimal energy recovery system synthesis especially for constrained waste heat recovery problems that exhibit several process constraints in matching hot and cold streams, for example, as identified above.
The two main methods that are currently in use in academia and in commercial products to attempt to address such issues are: mathematical programming and thermodynamic-heuristics based pinch technology. Both methods, however, fail to solve the problem of finding energy targets for waste heat recovery constrained problems, systematically, under all reasonably possible combinations of solutions using steam specific minimum temperature approach values or account for insertion of new carrier streams and/or changing specific streams flow, supply temperatures and target temperatures simultaneously, without manual iteration.
It is recognized by the inventor that driving force variation in constrained energy recovery systems can come from the combined effect of: the system's process conditions, the hot and cold stream minimum temperature approach value(s) ΔTmin, and the nature of the streams included in the waste heat recovery problem. As such, it is recognized by the inventor that the optimal selection of the optimal solution to the constrained waste heat recovery system that can provide a significant positive impact on energy consumption, utility selection, utility systems, and/or energy recovery systems capital investment, would be a great contribution, especially in the current energy crisis market.
Currently, there are no methods or associated algorithms that can handle the theoretical, practical, and more economical non-thermodynamically constrained waste heat recovery targeting problem under variable process conditions to target and find optimal conditions without manual iterations (manual data entry) and in a user-friendly manner.
NZ Patent No. 527,244 (July 2004) and WO Application No. 2005/1,010,783 (February 2005) have addressed the problem of energy targeting to find an optimal driving force distribution due to process conditions optimization, but only for a global ΔTmin, and not for stream-specific ΔTmini. Other prior teachings have suggested using heuristics to find the energy utility targets and an optimal driving force distribution at constant process conditions and stream-dependent ΔTmin. Still other prior teachings have tried to find an optimal driving force distribution through process conditions optimization at a fixed global ΔTmin using mathematical programming.
U.S. Non-Provisional patent application Ser. No. 11/768,084, filed Jun. 25, 2007, by Noureldin et al., has addressed the problem of the energy targeting and finding the optimal process conditions taking into consideration heat exchangers network capital cost and pareto calculation, but in the energy targeting calculation portion it did not explicitly describe and analyze application to non-thermodynamics-based constraints such as, for example, corrosion, scaling, the physical existence of streams, safety and hazardous issues, health and environmental compliances and operability or other constraints that were not embedded in the list of the problem constraints.
In most industrial processes, it is at least inefficient, if not impractical, to require that all heat exchangers (and thus, all process streams and utilities) obey the same global minimum value for driving forces, since streams (process and utility), in general, have very different heat transfer coefficients. Quite often, the difference in film heat transfer coefficients can be several orders of magnitude. Thus, some heat exchangers require large ΔTmin values in order to avoid requiring an excessive heat transfer area, while other units will manage well with much smaller ΔTmin values. Accordingly, recognized by the inventor is that a hot stream-specific minimum temperature approach optimal set can lead to much better energy consumption targets, especially for non-thermodynamically constrained waste heat recovery problems. Further, when considering retrofitting, the same problems exist with still no practical solution to simultaneously finding energy targets under all possible combinations of different process conditions, while using stream-specific minimum temperature approach values (ΔTmini).
Some scientists have recognized the need for at least assigning individual contributions to the minimum driving forces for each stream and utility, based upon the heat transfer coefficient of matched streams. These ΔTmin contributions could possibly, not only reflect heat transfer conditions, but could be used to represent the need for expensive materials of construction, heat exchanger types, etc. These methods, however, are based upon heuristics and are iterative, lack systemization, and do not consider the possible changes in process conditions that can result in significant changes in the energy system driving force distribution.
It has also been recognized by the inventor that in most industrial processes, it is at least inefficient, if not impractical, to properly target and find the most optimal conditions for waste heat recovery systems by employing an analysis which assumes non-constrained matches among hot and cold process streams. Further, while some researchers and commercial software are attempting to target for constrained energy recovery systems using linear programming, they do not consider the effect under even a reasonable expected set of possible combinations of process modifications that can include parametric and structural modifications such as, for example, the addition of one or more heat carrier streams, which can render better solutions of the constrained problem.
Accordingly, recognized by the inventor is that it would be beneficial to have a system, method and program product that can employ an algorithm that utilizes both process conditions manipulation including the creation of new heat carrier streams, and stream-specific minimum temperature approach values ΔTmini manipulation to target for energy consumption of constrained waste heat recovery problems under all possible solutions that can substantially reduce the effect of the constraints on the waste heat recovered values and that can be employed to find the best solution to reach the desired target systematically, without iteration or enumeration, without customized modeling, and in a user friendly manner.